Fabrication of metallic patterns by microstencil lithography on polymer surfaces suitable as microelectrodes in integrated microfluidic systems

Microstencil lithography, i.e. local deposition of micrometer scale patterns through small shadow masks, is a promising method for metal micropattern definition on polymer substrates that cannot be structured using organic-solvent-based photoresist technology. We propose to apply microstencil lithography to fabricate microelectrodes on flat and 3D polymer substrates, such as PMMA or SU-8, which form parts of microfluidic systems with integrated microelectrodes. Microstencil lithography is accompanied by two main issues when considered for application as a low-cost, reproducible alternative to standard photolithography on polymer substrates. In this paper we assess in detail (i) the reduction of aperture size (clogging) after several metal evaporation steps and corresponding change of deposited pattern size and (ii) loss in the resolution (blurring) of the deposited microstructures when there is a several micrometers large gap between the stencil membrane and the substrate. The clogging of stencil apertures induced by titanium and copper evaporation was checked after each evaporation step, and it was determined that approximately 50% of the thickness of the evaporated metals was deposited on the side walls of the stencil apertures. The influence of a gap on the deposited structures was analyzed by using 18 um thick SU-8 spacers placed between the microstencil and the substrate. The presence of an 18 um gapmade the deposited structures notably blurred. The blurring mechanism of deposited structures is discussed based on a simplified geometrical model. The results obtained in this paper allow assessing the feasibility of using stencil-based lithography for unconventional surface patterning, which shows the limits of the proposed method, but also provides a guideline on a possible implementation for combined polymer-electrode microsystems, where standard photoresist technology fails

Nanostenciling for fabrication and interconnection of nanopatterns and microelectrodes

Stencil lithography is used for patterning and connecting nanostructures with metallic microelectrodes in ultrahigh vacuum. Microelectrodes are fabricated by static stencil deposition through a thin silicon nitride membrane. Arbitrary nanoscale patterns are then deposited at a predefined position relative to the microelectrodes, using as a movable stencil mask an atomic force microscopy (AFM) cantilever in which apertures have been drilled by focused ion beam. Large scale AFM imaging, combined with the use of a high precision positioning table, allows inspecting the microelectrodes and positioning the nanoscale pattern with accuracy better than 100 nm

IMPROVING LOCAL WEATHER FORECASTS FOR AGRICULTURAL APPLICATIONS

ABSTRACT For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems usually the forecasts of the nearest meteorological station are used whereas measurements are taken from the systems location. The objective of this study is to evaluate the reduction of the forecast error for a specific agricultural system. Three weather variables , that are most relevant for greenhouse systems are studied: temperature, wind speed, and global radiation. Two procedures are used consecutively: diurnal bias correction and local adaptive forecasting. For each of the variables both bias and standard deviation were reduced. In general, if local measurements are reliable, forecast errors can be reduced considerably

On the increase of predictive performance with high-level data fusion

The combination of the different data sources for classification purposes, also called data fusion, can be done at different levels: low-level, i.e. concatenating data matrices, medium-level, i.e. concatenating data matrices after feature selection and high-level, i.e. combining model outputs. In this paper the predictive performance of high-level data fusion is investigated. Partial least squares is used on each of the data sets and dummy variables representing the classes are used as response variables. Based on the estimated responses for data set j and class k, a Gaussian distribution is fitted. A simulation study is performed that shows the theoretical performance of high-level data fusion for two classes and two data sets. Within group correlations of the predicted responses of the two models and differences between the predictive ability of each of the separate models and the fused models are studied. Results show that the error rate is always less than or equal to the best performing subset and can theoretically approach zero. Negative within group correlations always improve the predictive performance. However, if the data sets have a joint basis, as with metabolomics data, this is not likely to happen. For equally performing individual classifiers the best results are expected for small within group correlations. Fusion of a non-predictive classifier with a classifier that exhibits discriminative ability lead to increased predictive performance if the within group correlations are strong. An example with real life data shows the applicability of the simulation results

Canonical correlation analysis of multiple sensory directed metabolomics data blocks reveals corresponding parts between data blocks.

Multiple analytical platforms are frequently used in metabolomics studies. The resulting multiple data blocks contain, in general, similar parts of information which can be disclosed by chemometric methods. The metabolites of interest, however, are usually just a minor part of the complete data block and are related to a response of interest such as quality traits. Concatenation of data matrices is frequently used to simultaneously analyze multiple data blocks. Two main problems may occur with this approach: 1) the number of variables becomes very large in relation to the number of observations which may deteriorate model performance, and 2) scaling issues between the data blocks need to be resolved. Therefore, a method is proposed that circumvents direct concatenation of two data matrices but does uncover the shared and distinct parts of the data sets in relation to quality traits. The relevant part of the data blocks with respect to the quality trait of interest is revealed by partial least squares regression on each of the data blocks. The score vectors of both models that are predictive for the quality trait are then used in a canonicalcorrelationanalysis. Highly correlating score vectors indicate parts of the data blocks that are closely related. By inspecting the relevant loading vectors, the metabolites of interest are reveale

ASSESSMENT OF MICROSTENCILING TECHNIQUE FOR LOW-COST PRODUCTION OF MICROELECTRODES

Shadow mask evaporation is a powerful technique which enables us to make micro- or nano-structures on substrates which are not feasible for conventional photolithography processes, such as plastics, pre-structured substrate, biologically/chemically modified surfaces, etc. In this project, we investigated (i) the transformation of apertures of microstencil mask after several metal deposition steps, and (ii) the resolution of deposited microstructures with/without a gap between a stencil mask and a substrate. The purpose was to asses the microstenciling technique as low-cost production process of microelectrodes onto plastic substrates, which could be applied, for instance, as sensor electrodes on BioMEMS, etc

Approximate solution to a hybrid model with stochastic volatility: a singular-perturbation strategy

We study a hybrid model of Sch¨obel-Zhu-Hull-White-type from a singular-perturbationanalysis perspective. The merit of the paper is twofold: On one hand, we find boundary conditions for the deterministic non-linear degenerate parabolic partial differential equation for the evolution of the stock price. On the other hand, we combine two-scales regular- and singular-perturbation techniques to find an approximate solution to the pricing PDE. The aim is to produce an expression that can be evaluated numerically very fast